Programming with Python

Graded Project - 2

Marketing Data Analysis

# Importing required libraries for given data analysis
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
import warnings
warnings.filterwarnings('ignore')

1

Loading Market Dataset

# Importing Market data 
df = pd.read_csv('marketing_data.csv')
df

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Country
0	1826	1970	Graduation	Divorced	$84,835.00	0	0	6/16/14	0	189	...	6	1	0	0	0	0	0	1	0	SP
1	1	1961	Graduation	Single	$57,091.00	0	0	6/15/14	0	464	...	7	5	0	0	0	0	1	1	0	CA
2	10476	1958	Graduation	Married	$67,267.00	0	1	5/13/14	0	134	...	5	2	0	0	0	0	0	0	0	US
3	1386	1967	Graduation	Together	$32,474.00	1	1	5/11/14	0	10	...	2	7	0	0	0	0	0	0	0	AUS
4	5371	1989	Graduation	Single	$21,474.00	1	0	4/8/14	0	6	...	2	7	1	0	0	0	0	1	0	SP
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10142	1976	PhD	Divorced	$66,476.00	0	1	3/7/13	99	372	...	11	4	0	0	0	0	0	0	0	US
2236	5263	1977	2n Cycle	Married	$31,056.00	1	0	1/22/13	99	5	...	3	8	0	0	0	0	0	0	0	SP
2237	22	1976	Graduation	Divorced	$46,310.00	1	0	12/3/12	99	185	...	5	8	0	0	0	0	0	0	0	SP
2238	528	1978	Graduation	Married	$65,819.00	0	0	11/29/12	99	267	...	10	3	0	0	0	0	0	0	0	IND
2239	4070	1969	PhD	Married	$94,871.00	0	2	9/1/12	99	169	...	4	7	0	1	1	0	0	1	0	CA

2240 rows × 28 columns

Displaying 5 random samples

df.sample(5) # Collecting 5 Random samples of data

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Country
1115	10250	1947	Basic	Together	$28,389.00	0	0	11/20/12	49	1	...	2	7	0	0	0	0	0	0	0	SP
1923	6049	1982	Master	Together	$73,450.00	0	0	12/5/13	85	1142	...	11	3	1	0	0	1	0	0	0	SP
2104	6988	1986	Graduation	Married	$71,952.00	1	0	1/10/13	93	656	...	8	4	1	0	1	0	0	0	0	AUS
947	1340	1986	Graduation	Married	$92,910.00	0	0	4/24/14	42	551	...	13	1	0	1	1	1	0	0	0	SP
1995	8132	1975	PhD	Married	$60,631.00	1	1	4/5/13	88	565	...	8	7	0	1	0	0	0	0	0	GER

5 rows × 28 columns

Getting Info about Data

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 28 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   ID                   2240 non-null   int64 
 1   Year_Birth           2240 non-null   int64 
 2   Education            2240 non-null   object
 3   Marital_Status       2240 non-null   object
 4    Income              2216 non-null   object
 5   Kidhome              2240 non-null   int64 
 6   Teenhome             2240 non-null   int64 
 7   Dt_Customer          2240 non-null   object
 8   Recency              2240 non-null   int64 
 9   MntWines             2240 non-null   int64 
 10  MntFruits            2240 non-null   int64 
 11  MntMeatProducts      2240 non-null   int64 
 12  MntFishProducts      2240 non-null   int64 
 13  MntSweetProducts     2240 non-null   int64 
 14  MntGoldProds         2240 non-null   int64 
 15  NumDealsPurchases    2240 non-null   int64 
 16  NumWebPurchases      2240 non-null   int64 
 17  NumCatalogPurchases  2240 non-null   int64 
 18  NumStorePurchases    2240 non-null   int64 
 19  NumWebVisitsMonth    2240 non-null   int64 
 20  AcceptedCmp3         2240 non-null   int64 
 21  AcceptedCmp4         2240 non-null   int64 
 22  AcceptedCmp5         2240 non-null   int64 
 23  AcceptedCmp1         2240 non-null   int64 
 24  AcceptedCmp2         2240 non-null   int64 
 25  Response             2240 non-null   int64 
 26  Complain             2240 non-null   int64 
 27  Country              2240 non-null   object
dtypes: int64(23), object(5)
memory usage: 490.1+ KB

# Checking for duplicate values
len(df[df.duplicated()])

0

Views

• Given Market data has 2240 entries with 28 columns
• (Education,Marital_Status,Income,Dt_Customer,Country) are the 5 columns which are object typre remaining 23 columns are in int data type
• Income is the only column with null values
• Income need to be convert into int datatype
• Dt_Customer has to be converted into datetime format
• We don't have any duplicate values

2

Statistics of dataset

# Statistical Summary of data
df.describe()

	ID	Year_Birth	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	...	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain
count	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	...	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000
mean	5592.159821	1968.805804	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	27.062946	...	2.662054	5.790179	5.316518	0.072768	0.074554	0.072768	0.064286	0.013393	0.149107	0.009375
std	3246.662198	11.984069	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	41.280498	...	2.923101	3.250958	2.426645	0.259813	0.262728	0.259813	0.245316	0.114976	0.356274	0.096391
min	0.000000	1893.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2828.250000	1959.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	1.000000	...	0.000000	3.000000	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	5458.500000	1970.000000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	8.000000	...	2.000000	5.000000	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	8427.750000	1977.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	33.000000	...	4.000000	8.000000	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	11191.000000	1996.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	263.000000	...	28.000000	13.000000	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

8 rows × 23 columns

# Statistical summery of categorical data
df.describe(include = 'O')

	Education	Marital_Status	Income	Dt_Customer	Country
count	2240	2240	2216	2240	2240
unique	5	8	1974	663	8
top	Graduation	Married	$7,500.00	8/31/12	SP
freq	1127	864	12	12	1095

Varaiance of dataset

df.var()

ID                     1.054082e+07
Year_Birth             1.436179e+02
Kidhome                2.898725e-01
Teenhome               2.965219e-01
Recency                8.388237e+02
MntWines               1.132978e+05
MntFruits              1.581926e+03
MntMeatProducts        5.094743e+04
MntFishProducts        2.984325e+03
MntSweetProducts       1.704080e+03
MntGoldProds           2.721442e+03
NumDealsPurchases      3.733542e+00
NumWebPurchases        7.721252e+00
NumCatalogPurchases    8.544517e+00
NumStorePurchases      1.056873e+01
NumWebVisitsMonth      5.888606e+00
AcceptedCmp3           6.750283e-02
AcceptedCmp4           6.902615e-02
AcceptedCmp5           6.750283e-02
AcceptedCmp1           6.017993e-02
AcceptedCmp2           1.321939e-02
Response               1.269309e-01
Complain               9.291257e-03
dtype: float64

50% of data lies at

df.quantile(q = 0.5)

ID                     5458.5
Year_Birth             1970.0
Kidhome                   0.0
Teenhome                  0.0
Recency                  49.0
MntWines                173.5
MntFruits                 8.0
MntMeatProducts          67.0
MntFishProducts          12.0
MntSweetProducts          8.0
MntGoldProds             24.0
NumDealsPurchases         2.0
NumWebPurchases           4.0
NumCatalogPurchases       2.0
NumStorePurchases         5.0
NumWebVisitsMonth         6.0
AcceptedCmp3              0.0
AcceptedCmp4              0.0
AcceptedCmp5              0.0
AcceptedCmp1              0.0
AcceptedCmp2              0.0
Response                  0.0
Complain                  0.0
Name: 0.5, dtype: float64

Lower quarter of the data

df.quantile(q = 0.25)

ID                     2828.25
Year_Birth             1959.00
Kidhome                   0.00
Teenhome                  0.00
Recency                  24.00
MntWines                 23.75
MntFruits                 1.00
MntMeatProducts          16.00
MntFishProducts           3.00
MntSweetProducts          1.00
MntGoldProds              9.00
NumDealsPurchases         1.00
NumWebPurchases           2.00
NumCatalogPurchases       0.00
NumStorePurchases         3.00
NumWebVisitsMonth         3.00
AcceptedCmp3              0.00
AcceptedCmp4              0.00
AcceptedCmp5              0.00
AcceptedCmp1              0.00
AcceptedCmp2              0.00
Response                  0.00
Complain                  0.00
Name: 0.25, dtype: float64

Midddle of the data

df.quantile(q = 0.5)

ID                     5458.5
Year_Birth             1970.0
Kidhome                   0.0
Teenhome                  0.0
Recency                  49.0
MntWines                173.5
MntFruits                 8.0
MntMeatProducts          67.0
MntFishProducts          12.0
MntSweetProducts          8.0
MntGoldProds             24.0
NumDealsPurchases         2.0
NumWebPurchases           4.0
NumCatalogPurchases       2.0
NumStorePurchases         5.0
NumWebVisitsMonth         6.0
AcceptedCmp3              0.0
AcceptedCmp4              0.0
AcceptedCmp5              0.0
AcceptedCmp1              0.0
AcceptedCmp2              0.0
Response                  0.0
Complain                  0.0
Name: 0.5, dtype: float64

Upper quarter of the data

df.quantile(q = 0.75)

ID                     8427.75
Year_Birth             1977.00
Kidhome                   1.00
Teenhome                  1.00
Recency                  74.00
MntWines                504.25
MntFruits                33.00
MntMeatProducts         232.00
MntFishProducts          50.00
MntSweetProducts         33.00
MntGoldProds             56.00
NumDealsPurchases         3.00
NumWebPurchases           6.00
NumCatalogPurchases       4.00
NumStorePurchases         8.00
NumWebVisitsMonth         7.00
AcceptedCmp3              0.00
AcceptedCmp4              0.00
AcceptedCmp5              0.00
AcceptedCmp1              0.00
AcceptedCmp2              0.00
Response                  0.00
Complain                  0.00
Name: 0.75, dtype: float64

3

Finding Missing Values

df.isna().sum() #isna() gives boolean data of missing values and sum() to count number of missing values

ID                      0
Year_Birth              0
Education               0
Marital_Status          0
 Income                24
Kidhome                 0
Teenhome                0
Dt_Customer             0
Recency                 0
MntWines                0
MntFruits               0
MntMeatProducts         0
MntFishProducts         0
MntSweetProducts        0
MntGoldProds            0
NumDealsPurchases       0
NumWebPurchases         0
NumCatalogPurchases     0
NumStorePurchases       0
NumWebVisitsMonth       0
AcceptedCmp3            0
AcceptedCmp4            0
AcceptedCmp5            0
AcceptedCmp1            0
AcceptedCmp2            0
Response                0
Complain                0
Country                 0
dtype: int64

# Renaming the Income column (Income column name has extra space)
df = df.rename(columns = {' Income ':'Income'})

Only Income has 24 missing values

# Checking for which values are considering null
df_null = pd.DataFrame({'value' : df['Income'], 'Missing?' : df['Income'].isnull()}) 
df_null[df_null['Missing?']==True]

	value	Missing?
134	NaN	True
262	NaN	True
394	NaN	True
449	NaN	True
525	NaN	True
590	NaN	True
899	NaN	True
997	NaN	True
1096	NaN	True
1185	NaN	True
1213	NaN	True
1312	NaN	True
1515	NaN	True
1558	NaN	True
1693	NaN	True
1804	NaN	True
1858	NaN	True
1863	NaN	True
1880	NaN	True
1967	NaN	True
1983	NaN	True
2139	NaN	True
2165	NaN	True
2170	NaN	True

Views

• only NaN values are considered as a missing values
• for impute null values we need to remove extra charachters from the Income column.
• Then we fill the null values with 'median'

4

Removing extra special characters

df

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Country
0	1826	1970	Graduation	Divorced	$84,835.00	0	0	6/16/14	0	189	...	6	1	0	0	0	0	0	1	0	SP
1	1	1961	Graduation	Single	$57,091.00	0	0	6/15/14	0	464	...	7	5	0	0	0	0	1	1	0	CA
2	10476	1958	Graduation	Married	$67,267.00	0	1	5/13/14	0	134	...	5	2	0	0	0	0	0	0	0	US
3	1386	1967	Graduation	Together	$32,474.00	1	1	5/11/14	0	10	...	2	7	0	0	0	0	0	0	0	AUS
4	5371	1989	Graduation	Single	$21,474.00	1	0	4/8/14	0	6	...	2	7	1	0	0	0	0	1	0	SP
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10142	1976	PhD	Divorced	$66,476.00	0	1	3/7/13	99	372	...	11	4	0	0	0	0	0	0	0	US
2236	5263	1977	2n Cycle	Married	$31,056.00	1	0	1/22/13	99	5	...	3	8	0	0	0	0	0	0	0	SP
2237	22	1976	Graduation	Divorced	$46,310.00	1	0	12/3/12	99	185	...	5	8	0	0	0	0	0	0	0	SP
2238	528	1978	Graduation	Married	$65,819.00	0	0	11/29/12	99	267	...	10	3	0	0	0	0	0	0	0	IND
2239	4070	1969	PhD	Married	$94,871.00	0	2	9/1/12	99	169	...	4	7	0	1	1	0	0	1	0	CA

2240 rows × 28 columns

• Income Column have an extra symbol('$'). And column must be converted to int instead of object data type
• Dt_Customer column should convert into datetime type

df['Income'] = df['Income'].str.replace('$','') # Replacing '$' with ''
df['Income'] = df['Income'].str.replace(',','') # Replacing ',' with ''
df['Income'] = df['Income'].str.replace('.00','') # replacing '.00' with ''

# NaN values itself considering as a float type 
# So, We convert notnull values into int data type 
df.loc[df['Income'].notnull(),'Income'] = df.loc[df['Income'].notnull(),'Income'].apply(int) 

Imputing null values with the median value of the Income Column (Relate to 3 Question)

df['Income'] = df['Income'].fillna(df['Income'].median()).astype(int)

df['Income'].isna().sum() # Confirming null values

0

df.dtypes['Income'] # Confirming Data type of income column

dtype('int32')

Converting Dt_Customer datatype

df.dtypes['Dt_Customer'] # Finding Dt_Customer datatype. 

dtype('O')

#Converting object data type to datatime type
df['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'])

df['Dt_Customer'] 

0      2014-06-16
1      2014-06-15
2      2014-05-13
3      2014-05-11
4      2014-04-08
          ...    
2235   2013-03-07
2236   2013-01-22
2237   2012-12-03
2238   2012-11-29
2239   2012-09-01
Name: Dt_Customer, Length: 2240, dtype: datetime64[ns]

5

Finding Age of the Customers

# Creating Age column with the age of customer( current age = 2022 - 'Year_Birth')
df['Age'] = 2022 - df['Year_Birth']

# Displaying Age and Birth year of Customers
df[['ID','Year_Birth','Age']]

	ID	Year_Birth	Age
0	1826	1970	52
1	1	1961	61
2	10476	1958	64
3	1386	1967	55
4	5371	1989	33
...	...	...	...
2235	10142	1976	46
2236	5263	1977	45
2237	22	1976	46
2238	528	1978	44
2239	4070	1969	53

2240 rows × 3 columns

Statistical summery of the Age

df['Age'].describe()

count    2240.000000
mean       53.194196
std        11.984069
min        26.000000
25%        45.000000
50%        52.000000
75%        63.000000
max       129.000000
Name: Age, dtype: float64

Views

• The youngest customer in the data with the age of 26 years
• The eldest customer in the data with the age of 129 years
• 50 % of the customers are less than 52 years old

6

Analysing the expenditure of customers on various products

# Creating a new column with total amount spent on various products
df['Total_Amount'] = df['MntWines'] + df['MntFruits'] + df['MntMeatProducts'] + df['MntFishProducts'] + df['MntSweetProducts'] + df['MntGoldProds']

df

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Country	Age	Total_Amount
0	1826	1970	Graduation	Divorced	84835	0	0	2014-06-16	0	189	...	0	0	0	0	0	1	0	SP	52	1190
1	1	1961	Graduation	Single	57091	0	0	2014-06-15	0	464	...	0	0	0	0	1	1	0	CA	61	577
2	10476	1958	Graduation	Married	67267	0	1	2014-05-13	0	134	...	0	0	0	0	0	0	0	US	64	251
3	1386	1967	Graduation	Together	32474	1	1	2014-05-11	0	10	...	0	0	0	0	0	0	0	AUS	55	11
4	5371	1989	Graduation	Single	21474	1	0	2014-04-08	0	6	...	1	0	0	0	0	1	0	SP	33	91
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2235	10142	1976	PhD	Divorced	66476	0	1	2013-03-07	99	372	...	0	0	0	0	0	0	0	US	46	689
2236	5263	1977	2n Cycle	Married	31056	1	0	2013-01-22	99	5	...	0	0	0	0	0	0	0	SP	45	55
2237	22	1976	Graduation	Divorced	46310	1	0	2012-12-03	99	185	...	0	0	0	0	0	0	0	SP	46	309
2238	528	1978	Graduation	Married	65819	0	0	2012-11-29	99	267	...	0	0	0	0	0	0	0	IND	44	1383
2239	4070	1969	PhD	Married	94871	0	2	2012-09-01	99	169	...	0	1	1	0	0	1	0	CA	53	1078

2240 rows × 30 columns

# Total_Amount with various products for each customer
df[['MntWines', 'MntFruits','MntMeatProducts', 'MntFishProducts', 'MntSweetProducts','MntGoldProds','Total_Amount']]

	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds	Total_Amount
0	189	104	379	111	189	218	1190
1	464	5	64	7	0	37	577
2	134	11	59	15	2	30	251
3	10	0	1	0	0	0	11
4	6	16	24	11	0	34	91
...	...	...	...	...	...	...	...
2235	372	18	126	47	48	78	689
2236	5	10	13	3	8	16	55
2237	185	2	88	15	5	14	309
2238	267	38	701	149	165	63	1383
2239	169	24	553	188	0	144	1078

2240 rows × 7 columns

Finding how much percentage of money spent on various products by each customer

# finding percentage of each products with the total_amount
wines = (df['MntWines'] / df['Total_Amount']) * 100
fruits = (df['MntFruits'] / df['Total_Amount']) * 100
meat = (df['MntMeatProducts'] / df['Total_Amount']) * 100
fish = (df['MntFishProducts'] / df['Total_Amount']) * 100
sweet = (df['MntSweetProducts'] / df['Total_Amount']) * 100
gold = (df['MntGoldProds'] / df['Total_Amount']) * 100
total = (df['Total_Amount']/df['Income']) * 100

# Creating data frame for percentage of money spent on various products
wines_df = wines.to_frame(name = 'wines')
fruits_df = fruits.to_frame(name = 'fruits')
meat_df = meat.to_frame(name = 'meat')
fish_df = fish.to_frame(name = 'fish')
sweet_df = sweet.to_frame(name = 'sweet')
gold_df = wines.to_frame(name = 'gold')
total_df = total.to_frame(name = 'total')

df2 = pd.concat([df['Total_Amount'],wines_df,fruits_df,meat_df,fish_df,sweet_df,gold_df],axis = 1)

df2

	Total_Amount	wines	fruits	meat	fish	sweet	gold
0	1190	15.882353	8.739496	31.848739	9.327731	15.882353	15.882353
1	577	80.415945	0.866551	11.091854	1.213172	0.000000	80.415945
2	251	53.386454	4.382470	23.505976	5.976096	0.796813	53.386454
3	11	90.909091	0.000000	9.090909	0.000000	0.000000	90.909091
4	91	6.593407	17.582418	26.373626	12.087912	0.000000	6.593407
...	...	...	...	...	...	...	...
2235	689	53.991292	2.612482	18.287373	6.821480	6.966618	53.991292
2236	55	9.090909	18.181818	23.636364	5.454545	14.545455	9.090909
2237	309	59.870550	0.647249	28.478964	4.854369	1.618123	59.870550
2238	1383	19.305857	2.747650	50.686913	10.773680	11.930586	19.305857
2239	1078	15.677180	2.226345	51.298701	17.439703	0.000000	15.677180

2240 rows × 7 columns

Finding how much percentage of money spent on each products by all customers

# Total amount of spend by all customers 
total_spend = df['Total_Amount'].sum()

# Finding how much percentage spent on various products
total_wineMnt = (df['MntWines'].sum()/total_spend)*100
total_fruitsMnt = (df['MntFruits'].sum()/total_spend)*100
total_meatMnt = (df['MntMeatProducts'].sum()/total_spend)*100
total_fishMnt = (df['MntFishProducts'].sum()/total_spend)*100
total_sweetMnt = (df['MntSweetProducts'].sum()/total_spend)*100
total_goldMnt = (df['MntGoldProds'].sum()/total_spend)*100
indexs = ['Wines','Fruits','Meatproducts','FishProducts','Sweets',' Gold']

#creating separate series for Amount spent on various products
df3 = pd.Series([total_wineMnt,total_fruitsMnt,total_meatMnt,total_fishMnt,total_sweetMnt,total_goldMnt],index=indexs)

df3.to_frame(name = 'Percentage of amount spent')

	Percentage of amount spent
Wines	50.171114
Fruits	4.341748
Meatproducts	27.558681
FishProducts	6.194380
Sweets	4.467320
Gold	7.266755

# pie chart on percentage of amount spent on various products
explode = (0.05,0.05,0.05,0.05,0.05,0.05)
ax = df3.plot.pie(autopct='%1.1f%%',explode = explode,
         figsize = (5,13),title = 'Percentage of amount spent',fontsize = 13)
ax.set_ylabel(None)
plt.show()

Views

• Wine is the product where most people spent their money on it.
• Less amount of money spent on fruits by the customers

7

Analysing the different purchasing mediums

# Displaying various medium of purchases
df[['NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases']]

	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases
0	1	4	4	6
1	1	7	3	7
2	1	3	2	5
3	1	1	0	2
4	2	3	1	2
...	...	...	...	...
2235	2	5	2	11
2236	1	1	0	3
2237	2	6	1	5
2238	1	5	4	10
2239	1	8	5	4

2240 rows × 4 columns

Total amount of purchases done by customers through different mediums

# Creating a new column with Total purchases
df['Total_Purchases'] = df['NumDealsPurchases'] + df['NumWebPurchases'] + df['NumCatalogPurchases'] + df['NumStorePurchases']

df[['ID','NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases','Total_Purchases']]

	ID	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	Total_Purchases
0	1826	1	4	4	6	15
1	1	1	7	3	7	18
2	10476	1	3	2	5	11
3	1386	1	1	0	2	4
4	5371	2	3	1	2	8
...	...	...	...	...	...	...
2235	10142	2	5	2	11	20
2236	5263	1	1	0	3	5
2237	22	2	6	1	5	14
2238	528	1	5	4	10	20
2239	4070	1	8	5	4	18

2240 rows × 6 columns

Finding contribution of each channel to the total purchases

# Summing total purchases done through different channels
NumDeals = df['NumDealsPurchases'].sum()
NumWeb = df['NumWebPurchases'].sum()
NumCatalog = df['NumCatalogPurchases'].sum()
NumStore = df['NumStorePurchases'].sum()
channels = [NumDeals,NumWeb,NumCatalog,NumStore]
channel_index = ['DealPurchases','WebPurchases','CatalogPurchases','StorePurchases']

df4 = pd.Series(channels,index = channel_index)
df4.to_frame(name = 'TotalNumPurchases')

	TotalNumPurchases
DealPurchases	5208
WebPurchases	9150
CatalogPurchases	5963
StorePurchases	12970

# Sum of total purchases
SumTotalPurchases = df['Total_Purchases'].sum()

# Finding percentage of different channels to the total purchases
Dealpercent = (NumDeals/SumTotalPurchases)*100
Webpercent = (NumWeb/SumTotalPurchases)*100
Catalogpercent = (NumCatalog/SumTotalPurchases)*100
Storepercent = (NumStore/SumTotalPurchases)*100
Channel_percent = [Dealpercent,Webpercent,Catalogpercent,Storepercent]

#creating separate series for different channels
df5 = pd.Series(Channel_percent,index = channel_index)
df5.to_frame(name = 'Percentage of Purchases')

	Percentage of Purchases
DealPurchases	15.643868
WebPurchases	27.484906
CatalogPurchases	17.911748
StorePurchases	38.959479

# Pie chart on total purchases
explode = (0.05,0.05,0.05,0.1)
ax1 = df5.plot.pie(autopct='%1.1f%%',explode = explode,
         figsize = (5,13),title = 'Percentage of Purchases',fontsize = 13)
ax1.set_ylabel(None)
plt.show()

Views

• Most people are like to by products on stores
• Later, Web is the channel where most people by products

8

Analysing Compaigns

# Displaying various campaigns done
df[['AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2']]

	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2
0	0	0	0	0	0
1	0	0	0	0	1
2	0	0	0	0	0
3	0	0	0	0	0
4	1	0	0	0	0
...	...	...	...	...	...
2235	0	0	0	0	0
2236	0	0	0	0	0
2237	0	0	0	0	0
2238	0	0	0	0	0
2239	0	1	1	0	0

2240 rows × 5 columns

For finding sucessful campaigns we need to sum the peoples who like the offers in particular campaigns

cmp1 = df['AcceptedCmp1'].sum()
cmp2 = df['AcceptedCmp2'].sum()
cmp3 = df['AcceptedCmp3'].sum()
cmp4 = df['AcceptedCmp4'].sum()
cmp5 = df['AcceptedCmp5'].sum()
cmp_list = [cmp1,cmp2,cmp3,cmp4,cmp5]
cmp_index = ['Camp1','Camp2','Camp3','Camp4','Camp5']

#creating separate series for campaigns
cmp_series = pd.Series(cmp_list,index = cmp_index)

# Campaign dataframe
camp_df = cmp_series.to_frame(name = 'Offer Accepted by')
camp_df

	Offer Accepted by
Camp1	144
Camp2	30
Camp3	163
Camp4	167
Camp5	163

# Using bar chat to display number of offfer acceptances in each campaigns
ax = camp_df.plot.bar(color = 'orange')
ax.set_xlabel('Camps',fontsize = 13)
ax.set_ylabel('Number of people accepted offer',fontsize = 13)
ax.set_title('Camps Vs Number of Acceptances')
plt.show()

Views

• Campaign 4 is the most successful campaign with 167 offer acceptancy
• Later, Camp 3 and 5 with 163 offer acceptancy
• Campaign 2 is disastered with only 30 offer acceptancy

9

Analysing revenue generated by various products

# Displaying different products
df[['MntWines', 'MntFruits','MntMeatProducts', 'MntFishProducts', 'MntSweetProducts','MntGoldProds']]

	MntWines	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds
0	189	104	379	111	189	218
1	464	5	64	7	0	37
2	134	11	59	15	2	30
3	10	0	1	0	0	0
4	6	16	24	11	0	34
...	...	...	...	...	...	...
2235	372	18	126	47	48	78
2236	5	10	13	3	8	16
2237	185	2	88	15	5	14
2238	267	38	701	149	165	63
2239	169	24	553	188	0	144

2240 rows × 6 columns

#Summing each products revenue 
rev_wines = df['MntWines'].sum()
rev_fruits = df['MntFruits'].sum()
rev_meatprod = df['MntMeatProducts'].sum()
rev_fishprod = df['MntFishProducts'].sum()
rev_sweets = df['MntSweetProducts'].sum()
rev_gold = df['MntGoldProds'].sum()
rev_list = [rev_wines,rev_fruits,rev_meatprod,rev_fishprod,rev_sweets,rev_gold]
rev_index = ['Wines','Fruits','Meat','Fish','Sweets','Gold']

#creating separate series for revenue
rev_series = pd.Series(rev_list,index = rev_index)

# Creating dataframe for rev_series
rev_df = rev_series.to_frame(name = 'total_revenue')
rev_df

	total_revenue
Wines	680816
Fruits	58917
Meat	373968
Fish	84057
Sweets	60621
Gold	98609

# Plotting bar chart to visualize the total revenue generated by each products
ax = rev_df.plot.bar()
ax.set_xlabel('Products',fontsize = 14)
ax.set_ylabel('Total Revenue',fontsize = 14)
ax.set_title('Products Vs Total Revenue')
plt.show()

Views

• Wines is the product which generate most revenue
• meat takes next place for revenue genration
• Fruits is the one which generates minimum revenue

10

Analysing Age and Response of customer in last campaign

#creating seperate dataframe for age and responses
Res_df = df[['Age','Response']]
Res_df

	Age	Response
0	52	1
1	61	1
2	64	0
3	55	0
4	33	1
...	...	...
2235	46	0
2236	45	0
2237	46	0
2238	44	0
2239	53	1

2240 rows × 2 columns

# Separate dataframe for people who accepted offer(Response = 1)
accep_df = Res_df[Res_df['Response'] == 1]
accep_df

	Age	Response
0	52	1
1	61	1
4	33	1
5	64	1
6	68	1
...	...	...
2195	55	1
2196	55	1
2212	39	1
2216	62	1
2239	53	1

334 rows × 2 columns

Age Vs Who Accept offer

#Plotting displot for Age who accept the offer
ax = sn.displot(accep_df['Age'],color = 'Teal',kde = True)

Views

• Peoples who have age around 48 to 53 give more responses in last campaign

# Grouping Responses by age of the customers
age_group = df.groupby(by = 'Age')['Response']
nres = age_group.sum()      # Sum of the number of responses
re_df = nres.to_frame(name = 'nres')

# Bar plot to visualize Age Vs Number of responses
plt.figure(figsize = (15,10))
ax = nres.plot.bar(color = 'Grey')
ax.set_xlabel('Age',fontsize = 13)
ax.set_ylabel('Number of responses',fontsize = 13)
ax.set_title('Age Vs Number of responses')
plt.show()

Views

• More responses given by people who has age 52(total 16 responses)

# Scatter plot to visualize the Response vs Age
plt.figure(figsize = (10,7))
sn.scatterplot(df['Response'],df['Age'],color = 'green')
plt.show()

Views

• In Response Vs Age, There is no any particular pattern mostly people with same age groups accept offer and also reject offer

11

Analysing Response with the country

df[['Response','Country']]

	Response	Country
0	1	SP
1	1	CA
2	0	US
3	0	AUS
4	1	SP
...	...	...
2235	0	US
2236	0	SP
2237	0	SP
2238	0	IND
2239	1	CA

2240 rows × 2 columns

# Grouping country with the response
country_response = df.groupby(by = 'Country')['Response']

#Summing responses with respect to country
responses = country_response.sum()

res_df = responses.to_frame(name = 'Responses')
res_df

	Responses
Country
AUS	23
CA	38
GER	17
IND	13
ME	2
SA	52
SP	176
US	13

# Plotting bar chart to visualize the Response with respect to country
ax = res_df.plot(kind = 'bar',color = 'black',title = 'Country Vs Number of acceptances')
ax.set_ylabel('Number of acceptances')
plt.show()

Views

• 'SP' is the country where most people accept the last campaign offer
• 'ME' is the country which less offer acceptancy in last campaign

12

Analysing relation between Dependents and total amount spent and total purchases

Dependents = Kidhome + Teenhome

df[['Total_Amount','Kidhome','Teenhome']]

	Total_Amount	Kidhome	Teenhome
0	1190	0	0
1	577	0	0
2	251	0	1
3	11	1	1
4	91	1	0
...	...	...	...
2235	689	0	1
2236	55	1	0
2237	309	1	0
2238	1383	0	0
2239	1078	0	2

2240 rows × 3 columns

# Creating dependents dataframe
dependent = df['Kidhome'] + df['Teenhome']
dependents = dependent.to_frame(name = 'Dependents')
dependents

	Dependents
0	0
1	0
2	1
3	2
4	1
...	...
2235	1
2236	1
2237	1
2238	0
2239	2

2240 rows × 1 columns

# Concate dependents and total amount spent
spent_df = pd.concat([df['Total_Amount'],dependents],axis = 1)
spent_df

	Total_Amount	Dependents
0	1190	0
1	577	0
2	251	1
3	11	2
4	91	1
...	...	...
2235	689	1
2236	55	1
2237	309	1
2238	1383	0
2239	1078	2

2240 rows × 2 columns

# Grouping Total amount by dependents 
dep_group = spent_df.groupby(by = 'Dependents')['Total_Amount']

#Summing total amount by dependents
total_sum = dep_group.sum()

dep_df = total_sum.to_frame(name = 'Total') # Creating dependent dataframe

dep_df

	Total
Dependents
0	705647
1	533243
2	103544
3	14554

# Bar plot to visualize the dependents vs total amount 
ax = dep_df.plot(kind = 'bar', color = 'brown',title = 'Dependents Vs Total amount spent')
ax.set_ylabel('Total amount spent',fontsize  = 13)
ax.set_xlabel('Dependents',fontsize = 13)
plt.show()

Views

• People who have 0 dependents spent most money on products
• Peoples with 3 dependents spent less money on products

Finding total purchases with respect to dependents

# Creating dataframe with total purchases and dependents
purchases_df = pd.concat([df['Total_Purchases'],dependents],axis = 1)
purchases_df

	Total_Purchases	Dependents
0	15	0
1	18	0
2	11	1
3	4	2
4	8	1
...	...	...
2235	20	1
2236	5	1
2237	14	1
2238	20	0
2239	18	2

2240 rows × 2 columns

# Grouping Total purchases by Dependents
purchase_group = purchases_df.groupby(by = 'Dependents')['Total_Purchases']

# Summing number of purchases with dependents
purchase_df = purchase_group.sum().to_frame(name = 'Npurchases')
purchase_df

	Npurchases
Dependents
0	11201
1	16334
2	5137
3	619

# Bar plot to visualize the Dependents vs Number of purchases
ax = purchase_df.plot(kind='bar',color = 'green')
ax.set_xlabel('Dependents',fontsize = 13)
ax.set_ylabel('Num of purchases',fontsize = 13)
ax.set_title('Dependents vs Number of purchases')
plt.show()

Views

• People with 1 dependent made more purchases
• peoplw with 3 dependents made less purchases

13

Correlation analysis

df.corr() # corr() gives correlation matrix for the data

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	...	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Age	Total_Amount	Total_Purchases
ID	1.000000	0.000028	0.008441	0.002406	-0.002580	-0.046524	-0.022878	0.004600	-0.004437	-0.024475	...	-0.036040	-0.025387	-0.007517	-0.021614	-0.015061	-0.021968	0.033883	-0.000028	-0.018054	-0.023834
Year_Birth	0.000028	1.000000	-0.152049	0.230176	-0.352111	-0.019871	-0.157773	-0.017917	-0.030872	-0.041625	...	0.061774	-0.060510	0.007123	-0.005930	-0.006539	0.021325	-0.030128	-1.000000	-0.111306	-0.168304
Income	0.008441	-0.152049	1.000000	-0.386325	0.030732	0.007828	0.524814	0.394071	0.532721	0.410228	...	-0.012673	0.181845	0.291812	0.233436	0.090220	0.113896	-0.027187	0.152049	0.608406	0.522006
Kidhome	0.002406	0.230176	-0.386325	1.000000	-0.036133	0.008827	-0.496297	-0.372581	-0.437129	-0.387644	...	0.014674	-0.161600	-0.205634	-0.172339	-0.081716	-0.080008	0.040207	-0.230176	-0.556669	-0.477895
Teenhome	-0.002580	-0.352111	0.030732	-0.036133	1.000000	0.016198	0.004846	-0.176764	-0.261160	-0.204187	...	-0.042677	0.038886	-0.191050	-0.140090	-0.015605	-0.154446	0.003138	0.352111	-0.138384	0.133163
Recency	-0.046524	-0.019871	0.007828	0.008827	0.016198	1.000000	0.016064	-0.004306	0.023056	0.001079	...	-0.032991	0.018826	0.000129	-0.019283	-0.001781	-0.198437	0.013231	0.019871	0.020433	0.005740
MntWines	-0.022878	-0.157773	0.524814	-0.496297	0.004846	0.016064	1.000000	0.389637	0.562667	0.399753	...	0.062202	0.373286	0.472613	0.354133	0.205907	0.247254	-0.039007	0.157773	0.891839	0.712789
MntFruits	0.004600	-0.017917	0.394071	-0.372581	-0.176764	-0.004306	0.389637	1.000000	0.543105	0.594804	...	0.014727	0.010152	0.215833	0.194748	-0.009773	0.125289	-0.005166	0.017917	0.614229	0.455461
MntMeatProducts	-0.004437	-0.030872	0.532721	-0.437129	-0.261160	0.023056	0.562667	0.543105	1.000000	0.568402	...	0.018272	0.102912	0.373769	0.309761	0.043033	0.236335	-0.023483	0.030872	0.842965	0.554229
MntFishProducts	-0.024475	-0.041625	0.410228	-0.387644	-0.204187	0.001079	0.399753	0.594804	0.568402	1.000000	...	0.000357	0.016843	0.199578	0.260762	0.002577	0.111331	-0.020953	0.041625	0.642818	0.469454
MntSweetProducts	-0.007642	-0.018133	0.391005	-0.370673	-0.162475	0.022670	0.386581	0.567164	0.523846	0.579870	...	0.001530	0.028641	0.259590	0.241818	0.009985	0.117372	-0.022485	0.018133	0.603016	0.472876
MntGoldProds	-0.013438	-0.061818	0.293149	-0.349595	-0.021725	0.016693	0.387516	0.392995	0.350609	0.422875	...	0.123091	0.022268	0.181021	0.166396	0.049990	0.139850	-0.030861	0.061818	0.524262	0.493314
NumDealsPurchases	-0.037166	-0.060846	-0.064903	0.221798	0.387741	-0.001098	0.010940	-0.132114	-0.122415	-0.139361	...	-0.023109	0.015594	-0.183248	-0.123244	-0.037695	0.002238	0.000420	0.060846	-0.065112	0.362334
NumWebPurchases	-0.018924	-0.145040	0.349172	-0.361647	0.155500	-0.010726	0.542265	0.296735	0.293761	0.293681	...	0.042176	0.155903	0.138684	0.155143	0.034188	0.148730	-0.016310	0.145040	0.519837	0.777831
NumCatalogPurchases	-0.003440	-0.121275	0.542453	-0.502237	-0.110769	0.025110	0.635226	0.487917	0.723827	0.534478	...	0.104730	0.139247	0.322321	0.308097	0.099852	0.220810	-0.020453	0.121275	0.778577	0.735198
NumStorePurchases	-0.014927	-0.128272	0.485102	-0.499683	0.050695	0.000799	0.642100	0.461758	0.479659	0.459855	...	-0.067577	0.179379	0.214790	0.183249	0.085189	0.039363	-0.016524	0.128272	0.674669	0.820259
NumWebVisitsMonth	-0.007446	0.121139	-0.506199	0.447846	0.134884	-0.021445	-0.320653	-0.418383	-0.539470	-0.446003	...	0.061211	-0.032126	-0.278113	-0.192502	-0.007196	-0.003987	0.019769	-0.121139	-0.500218	-0.312288
AcceptedCmp3	-0.036040	0.061774	-0.012673	0.014674	-0.042677	-0.032991	0.062202	0.014727	0.018272	0.000357	...	1.000000	-0.079512	0.080316	0.094751	0.072020	0.254258	0.008415	-0.061774	0.053385	0.020709
AcceptedCmp4	-0.025387	-0.060510	0.181845	-0.161600	0.038886	0.018826	0.373286	0.010152	0.102912	0.016843	...	-0.079512	1.000000	0.306526	0.251300	0.292210	0.177019	-0.027611	0.060510	0.253290	0.189331
AcceptedCmp5	-0.007517	0.007123	0.291812	-0.205634	-0.191050	0.000129	0.472613	0.215833	0.373769	0.199578	...	0.080316	0.306526	1.000000	0.403078	0.221533	0.326634	-0.009419	-0.007123	0.470058	0.217754
AcceptedCmp1	-0.021614	-0.005930	0.233436	-0.172339	-0.140090	-0.019283	0.354133	0.194748	0.309761	0.260762	...	0.094751	0.251300	0.403078	1.000000	0.175315	0.293982	-0.025499	0.005930	0.381523	0.220041
AcceptedCmp2	-0.015061	-0.006539	0.090220	-0.081716	-0.015605	-0.001781	0.205907	-0.009773	0.043033	0.002577	...	0.072020	0.292210	0.221533	0.175315	1.000000	0.169293	-0.011334	0.006539	0.135813	0.076980
Response	-0.021968	0.021325	0.113896	-0.080008	-0.154446	-0.198437	0.247254	0.125289	0.236335	0.111331	...	0.254258	0.177019	0.326634	0.293982	0.169293	1.000000	-0.001707	-0.021325	0.265298	0.155138
Complain	0.033883	-0.030128	-0.027187	0.040207	0.003138	0.013231	-0.039007	-0.005166	-0.023483	-0.020953	...	0.008415	-0.027611	-0.009419	-0.025499	-0.011334	-0.001707	1.000000	0.030128	-0.037058	-0.020583
Age	-0.000028	-1.000000	0.152049	-0.230176	0.352111	0.019871	0.157773	0.017917	0.030872	0.041625	...	-0.061774	0.060510	-0.007123	0.005930	0.006539	-0.021325	0.030128	1.000000	0.111306	0.168304
Total_Amount	-0.018054	-0.111306	0.608406	-0.556669	-0.138384	0.020433	0.891839	0.614229	0.842965	0.642818	...	0.053385	0.253290	0.470058	0.381523	0.135813	0.265298	-0.037058	0.111306	1.000000	0.753903
Total_Purchases	-0.023834	-0.168304	0.522006	-0.477895	0.133163	0.005740	0.712789	0.455461	0.554229	0.469454	...	0.020709	0.189331	0.217754	0.220041	0.076980	0.155138	-0.020583	0.168304	0.753903	1.000000

27 rows × 27 columns

We display correlation analysis visually using Heatmap

# Visualizing the correlation using heatmap
plt.figure(figsize = (20,15))
sn.heatmap(df.corr(),annot = True,cmap = 'Blues')

<AxesSubplot:>

Views

• Total amount have a strong correlation with the amount of wine sales. which is positively correlated.
• Total amount have positive correlation with meat products,fruits,sweet and gold respectively
• catalog purchases are mostly contribute the total amount later store and web purchases
• Store purchases are positively highly corellated with total purchases
• age doesn't have much corellation
• amount wine is negatively correlated to kids in home(-0.5)

14

Analysing complains with respect to Educational background

#Displaying Educational and Complain 
df[['Education','Complain']]

	Education	Complain
0	Graduation	0
1	Graduation	0
2	Graduation	0
3	Graduation	0
4	Graduation	0
...	...	...
2235	PhD	0
2236	2n Cycle	0
2237	Graduation	0
2238	Graduation	0
2239	PhD	0

2240 rows × 2 columns

# Grouping Complain by Education
Ed_group = df.groupby(by = 'Education')['Complain']

#Summing Number of complains 
Ed_df = Ed_group.sum().to_frame(name = 'Ncomplains')
Ed_df

	Ncomplains
Education
2n Cycle	4
Basic	0
Graduation	14
Master	2
PhD	1

# Bar plot to visualizing the education vs number of complains
ax = Ed_df.plot(kind = 'bar',color = 'Red')
ax.set_xlabel('Education',fontsize = 13)
ax.set_ylabel('Number of Complains',fontsize = 13)
ax.set_title('Education Vs NComplains',fontsize = 13)
plt.show()

Views

• Most number of complains given by the Graduational students
• There is no complains from Basic Studnets

15

Pair plot to analyse different data sets

#creating new dataset with various \ products and total amount
new_df = df[['Total_Amount','MntFruits', 'MntMeatProducts', 'MntFishProducts','MntSweetProducts','MntGoldProds']]
new_df

	Total_Amount	MntFruits	MntMeatProducts	MntFishProducts	MntSweetProducts	MntGoldProds
0	1190	104	379	111	189	218
1	577	5	64	7	0	37
2	251	11	59	15	2	30
3	11	0	1	0	0	0
4	91	16	24	11	0	34
...	...	...	...	...	...	...
2235	689	18	126	47	48	78
2236	55	10	13	3	8	16
2237	309	2	88	15	5	14
2238	1383	38	701	149	165	63
2239	1078	24	553	188	0	144

2240 rows × 6 columns

#Pairplot to various products and total amount with 'Response' as a hue parameter
sn.pairplot(df,vars = ['Total_Amount','MntFruits', 'MntMeatProducts', 'MntFishProducts',
                       'MntSweetProducts','MntGoldProds'],hue = 'Response')

<seaborn.axisgrid.PairGrid at 0x24dd54a7310>

#Pairplot to various products and total amount with 'Education' as a hue parameter
sn.pairplot(df,vars = ['Total_Amount','MntFruits', 'MntMeatProducts', 'MntFishProducts',
                       'MntSweetProducts','MntGoldProds'],hue = 'Education')

<seaborn.axisgrid.PairGrid at 0x24dd816f310>

#Pairplot to various products and total amount with 'Marital_status' as a hue parameter
sn.pairplot(df,vars = ['Total_Amount','MntFruits', 'MntMeatProducts', 'MntFishProducts',
                       'MntSweetProducts','MntGoldProds'],hue = 'Marital_Status')

<seaborn.axisgrid.PairGrid at 0x24ddaa26ee0>

Views

• There are more people who haven't respond for the last campaign.
• Total amount is linearly correlated to the various products
• More graduated customers spent more on various products. later phd holders spent more
• Together and married peoples are spent more on various products. Their expenditure is positively correlated